Senior Data Scientist

The Rundown AI, Inc.
London
7 months ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

The role of a Data Scientist at Dataiku is quite unique. Our Data Scientists not only code up solutions to real-world problems but also participate in client-facing endeavors throughout the customer journey. This includes supporting their discovery of the platform, helping integrate Dataiku with other tools and technologies, some user training, and co-developing data science projects from design to deployment.

Just as the non-technical skills are important, so too are the technical. Our Data Scientists work on the Dataiku platform every day. Aside from the visual tools, our team uses mostly Python and SQL, with occasional work in other languages (e.g., R, Pyspark, JavaScript, etc.). An ideal candidate is excited to learn complex new technologies and modeling techniques while being able to explain their work to other data scientists and clients.

Key Areas of Responsibility (What You’ll Do)

  • Scope and co-develop production-level data science projects with our customers across different industries and use cases
  • Help users discover and master the Dataiku platform via user training, office hours, and ongoing consultative support
  • Provide strategic input to the customer and account teams that help make our customers successful.
  • Provide data science expertise both to customers and internally to Dataiku’s sales and marketing teams
  • Develop custom Python-based “plugins” in collaboration with Solutions, R&D, and Product teams to enhance Dataiku’s functionality

Experience (What We’re Looking For):

  • Curiosity and a desire to learn new topics and skills
  • Empathy for others and an eagerness to share your knowledge and expertise with your colleagues, Dataiku’s customers, and the general public
  • The ability to clearly explain complex topics to technical as well as non-technical audiences
  • Over 5 years of experience with Python and SQL
  • Over 5 years of experience with building ML models and using ML tools (e.g., sklearn)
  • Experience with Consulting and/or Customer-facing Data Science roles
  • Familiarity with data visualization in Python, R
  • Understanding of underlying data systems such as Cloud architectures, Hadoop, or SQL

Bonus points for any of these:

  • Experience with Data Engineering or MLOps
  • Experience developing WebApps in Javascript, RShiny, or Dash
  • Experience building APIs
  • Experience using enterprise data science tools
  • Passion for teaching or public speaking

#LI-Hybrid


#J-18808-Ljbffr

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

New Machine Learning Employers to Watch in 2026: UK and Global Companies Driving ML Innovation

Machine learning (ML) has transitioned from a specialised field into a core business capability. In 2026, organisations across healthcare, finance, robotics, autonomous systems, natural language processing, and analytics are expanding their machine learning teams to build scalable intelligent products and services. For professionals exploring opportunities on www.MachineLearningJobs.co.uk , understanding the companies that are scaling, winning investment, or securing high‑impact contracts is crucial. This article highlights the new and high‑growth machine learning employers to watch in 2026, focusing on UK innovators, international firms with significant UK presence, and global platforms investing in machine learning talent locally.

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.